Stock Seasonality Chart

What is Stock Seasonality?

Stock seasonality refers to recurring patterns in stock and ETF performance during specific months of the year. These patterns emerge from analyzing years of historical monthly returns data. Famous seasonal effects include the "January Effect" (stocks tend to rise in January), "Sell in May and Go Away" (summer underperformance), and the "Santa Claus Rally" (year-end gains). Our free seasonality chart tool helps you analyze these patterns for any stock or ETF using real historical data.

How to Use the Seasonality Chart

Enter any stock symbol (like AAPL, MSFT, TSLA) or ETF (SPY, QQQ, IWM) to see its historical monthly performance. Look for months with consistently high win rates (70%+) and positive average returns for potential buying opportunities. Months with low win rates and negative average returns may indicate periods to be cautious. Combine seasonality analysis with technical indicators and fundamental research for best results.

The January Effect Explained

The January Effect is one of the most well-known seasonal anomalies in stock market history. Research suggests stocks, especially small-cap stocks, tend to outperform in January. This is attributed to tax-loss selling in December (investors selling losers for tax benefits, then reinvesting in January), year-end bonus investing, and institutional portfolio rebalancing. Use our seasonality tool to verify if specific stocks exhibit this pattern.

Sell in May and Go Away

"Sell in May and Go Away" is a trading adage suggesting the six-month period from November to April produces stronger stock returns than May through October. Historical data shows the S&P 500 has averaged higher returns during the "best six months." However, this varies significantly by individual stock and sector. Our seasonality chart lets you analyze whether specific stocks follow this pattern or buck the trend.

Key Seasonality Metrics Explained

• Average Return: Mean percentage return for each month across all years analyzed - shows typical performance
• Median Return: Middle value less affected by extreme outliers - often more representative than average
• Max/Min: Best and worst historical performance - shows the range of possible outcomes
• Years of Data: More years means more statistically significant patterns - 10+ years recommended

Best Practices for Seasonality Trading

• Use at least 10 years of data for reliable patterns (available with premium)
• Look for months with win rates above 70% for higher probability trades
• Combine seasonality with technical analysis and market conditions
• Consider sector rotation - different sectors peak in different months
• Remember: past performance doesn't guarantee future results
• Use seasonality as one factor among many in your trading decisions